| #define _CRT_SECURE_NO_WARNINGS | |
| #include <Windows.h> | |
| #include <Psapi.h> | |
| #include <TlHelp32.h> | |
| #include <iostream> | |
| DWORD GetLsassPid() { | |
| PROCESSENTRY32 entry; | |
| entry.dwSize = sizeof(PROCESSENTRY32); |
This script pulls lyrics for FLAC, MP3, and M4A files in a given music folder. It is not Beets-specific but works well with a Beets-managed library. The script supports incremental processing by marking folders as processed using a .lyrical file.
pip installed.pip install tqdm tenacity langdetect mutagen syncedlyrics
| #!/usr/bin/env python3 | |
| # If you have a linux md (software) RAID array that is reporting sector | |
| # mismatches, you can use this script to find which files are affected. | |
| # Update the `err` and `DEVICE` definitions below, and ensure you have | |
| # e2fsprogs installed. (This is for ext3 / ext4 filesystems). | |
| from typing import List, Tuple | |
| import subprocess | |
| from subprocess import CompletedProcess | |
| err = """ |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
DISCLAIMER: These steps CAN'T help you activate the China Government Edition (Enterprise G).
The official ISOs for the China Government Edition (Enterprise G) is (probably) not hosted on Microsoft's sources that you can access to download normal Windows or normal Enterprise editions, but in CMIT/CMGOS (which Microsoft collaborates with to develop this edition). Here's how to get the ISOs from official sources:
请输入SN号或设备编号): 0101111011111 (make sure that this has no spaces)请输入右侧验证码): (enter what's show in the blue box, which generates different codes on each load of the page (a type of CAPTCHA))验证 (verify).
Note: YouTube is serving thumbnails from 2 servers:
//img.youtube.com//i.ytimg.comExamples are with //i.ytimg.com server just because it’s shorter, no other particular reason. You can use both.
Player Background Thumbnail (480x360):
| { | |
| "enabled": true, | |
| "versionCode": 1, | |
| "versionName": "1.0", | |
| "forceUpdate": false, | |
| "title": "发现新版本", | |
| "message": "", | |
| "apkUrl": "", | |
| "announcementTitle": "公告", | |
| "announcementMessage": "灵岛小记正版QQ群:531375025", |
HADDOCK3 provides a powerful workflow to docke two or more protein structures and it provides multiple metrics as an output for quantifying molecular binding affinity. However, a full docking pipeline may take some time to complete.
To get binding affinity metrics for PDB files where the protein complex already exists, we don't necessarily need to perform a full docking workflow. Also, there is a need to understand binding affinity in protein complex files generated from multimer folding prediction tools like AlphaFold3, Boltz-1, or ESM3.
